Attention as a proxy of explanation: Model's attention [1]}, [2]}, [3]} could serve as a proxy of the rationale. Although attention is easy to obtain, it lacks faithfulness. An input associated with low attention weight can still significantly impact the prediction. In addition, recent works [4]}, [5]}, [6]}, [7]}, [8]} also find that the same prediction on an input could be generated by totally different attentions, which limits its applicability to explaining neural predictions. To improve the faithfulness of attentions, [9]}, [10]} regularize the hidden representations on which the attention is computed over; [11]} applies attention weights on losses of pre-defined individual rationale candidates' predictions. Nevertheless, rationales remain to be more faithful explanations due to their certification of exclusion.
